CN112737102A - Microcomputer anti-error processing method for artificial intelligent transformer substation - Google Patents
Microcomputer anti-error processing method for artificial intelligent transformer substation Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02B90/20—Smart grids as enabling technology in buildings sector
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
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Abstract
The 'after-the-fact tracing' of an accident can be realized by monitoring and analyzing behaviors of an operator in real time and intervening irregular operations and behaviors to realize integration of video monitoring and error prevention; the intelligent accident early warning system has an intelligent analysis function, monitors specific events and user behaviors, and achieves intelligent early warning of accidents. The method comprises the following steps: erecting a plurality of cameras on the site of the substation equipment, collecting video signals of all the cameras in the substation and transmitting the video signals to a computer; building an artificial intelligent OpenCV visual base and utilizing a GPU parallel processing technology; constructing a deep neural network to identify the image, and realizing analysis, judgment and monitoring of the behavior of an operator; the video monitoring, intelligent analysis, behavior monitoring and anti-misoperation locking technology are deeply integrated, when specific behaviors are found, an alarm is automatically generated, and monitoring personnel are informed to perform early warning, linkage and processing in time.
Description
Technical Field
The invention relates to an artificial intelligent substation microcomputer anti-misoperation processing method integrating a video intelligent identification technology, and belongs to the field of substation microcomputer anti-misoperation locking systems.
Background
In the traditional monitoring, a camera is an eye for security monitoring, and the work of video analysis, extraction, early warning, alarming, retrieval, fault diagnosis and the like is completed by human eyes, so that the traditional security monitoring is realized by the operation of a monitoring system by electronic eyes and an artificial brain. With the development of artificial intelligence and video processing technology, the application of intelligent identification technology is more and more extensive, images of a camera are collected in real time and further analyzed through upgrading and optimizing traditional video monitoring, and when a specific behavior is found, an alarm is automatically generated to inform monitoring personnel of early warning, linkage and processing in time.
The electric misoperation accident of the transformer substation can cause large-area power failure, equipment damage and even personal injury, and brings great economic loss to the country. And the anti-misoperation locking system can effectively prevent the accidents. The microcomputer five-prevention locking device realizes the following functions: 1. and the circuit breaker is prevented from being switched on and off by mistake in the operation process. 2. Preventing the isolating switch from being pulled on under load. 3. Prevent the charged hanging (closing) of the ground wire (grounding knife switch). 4. The circuit breaker (isolating switch) is prevented from being closed by a grounding wire (grounding knife switch). 5. Prevent the wrong entering into the electrified interval. The microcomputer five-prevention system locking device mainly comprises functional elements such as a host, an analog screen, a computer key, a mechanical coding lock and the like, and the locking equipment comprises a circuit breaker, an isolating switch, a grounding wire, a grounding knife switch and a barrier net door (a switch cabinet door). According to the actual state of the field equipment, five-prevention logic judgment is carried out on the switching operation, a correct switching operation ticket is opened, the operation sequence is transmitted to a computer key, and the user holds the computer key to unlock the field in sequence and then carries out switching operation on equipment such as a circuit breaker, an isolating switch, a grounding knife switch, a temporary grounding wire and a net door.
Along with the rapid development of electric power systems, the number of unattended substations is gradually increased, and in order to guarantee the safe operation of a power grid and solve the problem of safety precaution of the unattended substations, regional (city) and county power supply companies have implemented transformer substation remote video monitoring systems step by step, so that the effective monitoring of the operation state of the transformer substations is realized, and the video monitoring systems play an increasingly important role in the transformer substations.
Although the functions and the performance of the video monitoring system and the video monitoring equipment are greatly improved, the video monitoring system and the video monitoring equipment are still limited by some factors (the weakness of human beings, monitoring time, false alarm and missing report and the like), so that the whole system cannot meet the expectation of people. The traditional video monitoring system needs workers to concentrate on a monitor screen, so that valuable and even suspicious information can be obtained, and the situation of false report and missing report exists in the monitoring of the workers. Meanwhile, a large-scale video monitoring system also has many problems, for example, in the face of hundreds of monitoring lenses, a worker cannot obtain valuable information in time at all, and the video monitoring system loses the original prevention and intervention capability. In the traditional monitoring, a camera is the eye of security monitoring, and the video analysis, extraction, early warning, alarming, retrieval, fault diagnosis and other work are completed by human eyes, so the traditional security monitoring is realized by the eyes of an electronic camera and an artificial brain to complete the operation of a monitoring system. The 'after-the-fact tracing' of the accident cannot be realized, the specific event and the behavior of the user cannot be monitored, and the 'intelligent early warning' of the accident cannot be realized.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and provides a method for realizing video monitoring and anti-error integration by intervening irregular operation and behavior through real-time monitoring and behavior analysis of operators, so that 'after-the-fact tracing' of accidents can be realized; the intelligent accident early warning system has an intelligent analysis function, monitors specific events and user behaviors, and achieves intelligent early warning of accidents.
The technical solution of the invention is as follows:
a microcomputer anti-error processing method for an artificial intelligent substation comprises the following steps:
firstly, erecting a plurality of cameras on a transformer substation device site, collecting video signals of all the cameras in a transformer substation, transmitting the video signals to a computer, and storing the video signals by the computer;
secondly, an artificial intelligent OpenCV visual library is built, and the GPU is utilized for parallel processing, so that the processing speed of acquiring images of all cameras on a real-time acquisition site is increased;
thirdly, a deep neural network is constructed to identify the image, so that the behavior analysis, judgment and monitoring of operators are realized;
and fourthly, deeply fusing video monitoring, intelligent analysis, behavior monitoring and anti-misoperation locking technologies, automatically generating an alarm when a specific behavior is found, informing monitoring personnel of early warning in time, and enabling a microcomputer anti-misoperation locking system to be linked and processed.
Further, the behavior analysis, judgment and monitoring process in the third step is as follows:
by separating background and foreground targets in a scene, the targets in the scene are detected, extracted and tracked and behavior recognition is carried out, and once the target breaks away from a predefined behavior rule in the scene, a user presets an alarm rule in the scene, the system can automatically send out an alarm, and automatically pops up alarm information and sends out a prompt tone.
Furthermore, in the third step, a deep learning model is established by using a deep neural network, the weight amplitude is effectively reduced through weight attenuation in the training of the network, the overfitting of the network is prevented, and the acceleration is combined with GPU (graphics processing unit) so that more training data can be generated in the training process and the network can better perform the training data.
Further, the alarm rule in step three is:
and (3) intrusion detection: the method comprises the steps that a user sets a warning area, when a moving object enters the warning area, moves in the warning area or crosses a set warning line, an alarm is triggered, a moving target is marked out by an alarm frame, a monitoring picture prompts alarm information, simultaneous identification and simultaneous alarm of multiple moving targets are supported, and the moving object refers to a person or a vehicle;
and (3) behavior detection: the system monitors characteristic events and specific behaviors of the moving object, if the behavior of holding the object is too high, an alarm is triggered, the moving object is marked by an alarm frame, and the monitoring picture prompts alarm information.
Furthermore, the microcomputer anti-misoperation locking system sends the allowed equipment to the computer video analysis system to intelligently detect the user operation behavior, and when the allowed equipment is operated, the operation is prompted to be correct; when other equipment is operated, the voice prompts that the operation is wrong.
Furthermore, in the second step, the GPU is utilized to accelerate calculation, the workload of the calculation intensive part of the application program is transferred to the GPU, and the CPU runs other program codes, so that the running speed of the application program is improved.
Further, the GPU adopts an Invitta CUDA general parallel computing architecture.
The invention deeply fuses the intelligent video analysis technology and the microcomputer anti-misoperation locking technology, and utilizes the deep neural network to establish the deep learning model, thereby not only greatly improving the precision of image recognition, but also avoiding consuming a large amount of time to extract artificial features, and greatly improving the online operation efficiency. The switching operation of the operator is analyzed and monitored through intelligent video analysis, and the error prevention coefficient is further improved. On one hand, the system has the functions of video acquisition and storage, and realizes 'retrospective tracing' of accidents; on the other hand, the intelligent alarm system has an intelligent analysis function, monitors specific events and user behaviors, intelligently analyzes, intelligently judges and intelligently alarms, and achieves intelligent early warning of accidents. This project is through real time monitoring and behavioral analysis to operating personnel, error in can be timely, accurate discovery operation, master the daily running state of equipment, utilize artificial intelligence technique to intervene unnormalized operation and action, realize video monitoring and prevent the integration of mistake, will effectively alleviate monitoring personnel's work load, show the operating level who improves the computer and prevent mistake shutting system, the safe operation of guarantee unmanned on duty transformer substation, can promote transformer substation operation management level, further promote national grid company's management to automatic, intelligent orientation development.
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FIG. 1 is a system architecture diagram of the present invention.
Detailed Description
As shown in the figure, the microcomputer anti-misoperation processing method of the artificial intelligent substation comprises the following steps:
firstly, erecting a plurality of cameras on a transformer substation device site, collecting video signals of all the cameras in a transformer substation, transmitting the video signals to a computer, and storing the video signals by the computer;
step two, an artificial intelligent OpenCV visual library is built in a computer, the work load of the application program calculation intensive part is transferred to a GPU by using a GPU parallel processing technology, and the CPU runs other program codes, so that the running speed of video analysis is improved, and the GPU adopts an English-Weber CUDA general parallel computing frame;
thirdly, a deep neural network is constructed to identify the image, so that the behavior analysis, judgment and monitoring of operators are realized;
and fourthly, deeply fusing video monitoring, intelligent analysis, behavior monitoring and anti-misoperation locking technologies, automatically generating an alarm when a specific behavior is found, informing monitoring personnel of early warning in time, and enabling a microcomputer anti-misoperation locking system to be linked and processed.
Further, the behavior analysis, judgment and monitoring process in the third step is as follows:
by separating background and foreground targets in a scene, the targets in the scene are detected, extracted and tracked and behavior recognition is carried out, and once the target breaks away from a predefined behavior rule in the scene, a user presets an alarm rule in the scene, the system can automatically send out an alarm, and automatically pops up alarm information and sends out a prompt tone.
Furthermore, in the third step, a deep learning model is established by using a deep neural network, the weight amplitude is effectively reduced through weight attenuation in the training of the network, the overfitting of the network is prevented, and the acceleration is combined with GPU (graphics processing unit) so that more training data can be generated in the training process and the network can better perform the training data.
Further, the alarm rule in step three is:
and (3) intrusion detection: the method comprises the steps that a user sets a warning area, when a moving object enters the warning area, moves in the warning area or crosses a set warning line, an alarm is triggered, a moving target is marked out by an alarm frame, a monitoring picture prompts alarm information, simultaneous identification and simultaneous alarm of multiple moving targets are supported, and the moving object refers to a person or a vehicle;
and (3) behavior detection: the system monitors characteristic events and specific behaviors of the moving object, if the behavior of holding the object is too high, an alarm is triggered, the moving object is marked by an alarm frame, and the monitoring picture prompts alarm information.
Furthermore, the microcomputer anti-misoperation locking system sends the allowed equipment to the computer video analysis system to intelligently detect the user operation behavior, and when the allowed equipment is operated, the operation is prompted to be correct; when other equipment is operated, the voice prompts that the operation is wrong.
The above is a specific embodiment of the present invention, and is not intended to limit the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A microcomputer anti-error processing method for an artificial intelligent substation is characterized by comprising the following steps:
firstly, erecting a plurality of cameras on a transformer substation device site, collecting video signals of all the cameras in a transformer substation, transmitting the video signals to a computer, and storing the video signals by the computer;
secondly, an artificial intelligent OpenCV visual library is built, and the GPU is utilized for parallel processing, so that the processing speed of acquiring images of all cameras on a real-time acquisition site is increased;
thirdly, a deep neural network is constructed to identify the image, so that the behavior analysis, judgment and monitoring of operators are realized;
and fourthly, deeply fusing video monitoring, intelligent analysis, behavior monitoring and anti-misoperation locking technologies, automatically generating an alarm when a specific behavior is found, informing monitoring personnel of early warning in time, and enabling a microcomputer anti-misoperation locking system to be linked and processed.
2. The microcomputer anti-error processing method of the artificial intelligent substation according to claim 1, wherein the behavior analysis, judgment and monitoring process in the third step is as follows:
by separating background and foreground targets in a scene, the targets in the scene are detected, extracted and tracked and behavior recognition is carried out, and once the target breaks away from a predefined behavior rule in the scene, a user presets an alarm rule in the scene, the system can automatically send out an alarm, and automatically pops up alarm information and sends out a prompt tone.
3. The method for preventing the misoperation of the microcomputer of the artificial intelligent substation according to the claim 1, wherein in the third step, a deep learning model is established by using a deep neural network, the weight amplitude is effectively reduced through weight attenuation in the training of the network, the overfitting of the network is prevented, and the acceleration is combined with a GPU, so that more training data can be generated in the training process, and the network can better perform the training data.
4. The microcomputer anti-error processing method of the artificial intelligent substation according to claim 1, wherein the alarm rule in the third step is as follows:
and (3) intrusion detection: the method comprises the steps that a user sets a warning area, when a moving object enters the warning area, moves in the warning area or crosses a set warning line, an alarm is triggered, a moving target is marked out by an alarm frame, a monitoring picture prompts alarm information, simultaneous identification and simultaneous alarm of multiple moving targets are supported, and the moving object refers to a person or a vehicle;
and (3) behavior detection: the system monitors characteristic events and specific behaviors of the moving object, if the behavior of holding the object is too high, an alarm is triggered, the moving object is marked by an alarm frame, and the monitoring picture prompts alarm information.
5. The microcomputer anti-misoperation processing method for the artificial intelligent substation according to claim 1, wherein the step four microcomputer anti-misoperation locking system sends allowed equipment to the computer video analysis system to intelligently detect user operation behaviors, and when the allowed equipment is operated, the operation is prompted to be correct; when other equipment is operated, the voice prompts that the operation is wrong.
6. The method for preventing the misoperation of the microcomputer of the artificial intelligent substation according to claim 1, wherein in the second step, the GPU is used for accelerating the calculation, the workload of the calculation-intensive part of the application program is transferred to the GPU, and the CPU runs other program codes, so that the running speed of the application program is increased.
7. The method for preventing the error processing of the microcomputer of the artificial intelligent substation according to claim 1 or 6, wherein the GPU adopts an Invitta CUDA general parallel computing architecture.
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